Cross-Receiver Radio Frequency Fingerprint Identification Based on Contrastive Learning and Subdomain Adaptation

Radio frequency fingerprint (RFF) identification is emerging as an attractive paradigm for physical layer security. Despite the exceptional accuracy achieved by deep learning (DL) based schemes, few works consider the cross-receiver scenario. The performance deteriorates significantly when the model is deployed on new receivers directly. To this end, a cross-receiver RFF learning scheme is proposed. First, an unsupervised pre-training method based on contrastive learning is utilized to extract receiver-agnostic features. Then, the model is optimized by subdomain adaptation to further improve identification performance. The proposed scheme does not require multiple labeled datasets from different receivers. And experimental results indicate that the proposed scheme effectively alleviates performance degradation in the cross-receiver scenario.

Automated summary

Radio frequency fingerprint (RFF) identification is a promising approach for physical layer security. However, current deep learning (DL) based schemes often suffer from performance degradation in cross-receiver scenarios. To address this issue, a cross-receiver RFF learning scheme is proposed, which utilizes unsupervised pre-training and subdomain adaptation to enhance identification performance. Experimental results demonstrate that the proposed scheme effectively mitigates the performance degradation in cross-receiver scenarios.